Hi, I'm Parmeet

The Michaelis constant (Km) is central to enzyme kinetics, guiding variant selection, inhibitor screening, and metabolic modeling. However, Km obtained by nonlinear regression can be substantially inaccurate even when the reported standard error (SE) appears small. Common software reports SE but provides no accuracy metric. This gap is addressed by extending the accuracy confidence interval (ACI) framework to Km (ACI-Km) through a binding-isotherm formulation of the velocity-substrate fit. Given confidence intervals for concentration accuracy, the method quantifies how residual systematic uncertainties in enzyme and substrate concentrations (E0 and S0) propagate into the determined Km values and provides a probabilistic interval expected to enclose the accurate value. The approach requires no additional kinetic experiments and is directly applicable to existing datasets. Concentration-accuracy intervals can be estimated from calibration data, reagent specifications, or quality-control records. ACI-Km is valid across a wide range of E0/Km conditions, including relatively high E0. A free web application (https://aci.sci.yorku.ca) implements ACI-Km. Tests on synthetic and experimental datasets show that Km ± SE can severely underestimate uncertainty, whereas ACI provides more reliable accuracy bounds for decision-making, complementing rather than replacing traditional precision metrics by providing quantitative diagnostic bounds for concentration-related uncertainties in Km determination.